Best local AI models for NVIDIA Quadro K6000

The largest models that fit fully

The 30 largest of the 147 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.

Close, but only with CPU offload

These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.

How to read this

The NVIDIA Quadro K6000 is equipped with 12 GB of GDDR5 memory. This dedicated memory size determines which artificial intelligence models can run directly on your hardware. To run a model entirely on the graphics card, the model files and the active workspace must fit within this 12 GB limit. Running models locally on your hardware ensures complete privacy and removes reliance on external cloud services.

The quantization column indicates the compression level applied to each model. Raw models are often too large for workstation hardware, so they are compressed into smaller formats called quants. For example, DeepSeek-Coder-V2 16B and Kimi-VL A3B fit within 11.7 GB of memory when using the Q4_K_M quant. Similarly, Gemma 3 12B, Gemma 4 12B, and Mistral NeMo 12B fit within 11.8 GB of memory when using the Q6_K quant. Higher quant levels like Q8_0 provide better precision but require more space, as seen with Nemotron Nano 9B utilizing 11.4 GB of memory.

When a model exceeds the 12 GB physical memory limit of your graphics card, you can use CPU offloading. This process splits the workload between your graphics card and your system RAM. For instance, running FLUX.1 dev requires 14.4 GB of memory at FP8 or optimized settings, which utilizes 16.4 GB of system RAM. Larger models like Solar Pro and Codestral 22B require 16.1 GB at the Q4_K_M quant, which utilizes 18.1 GB of system RAM.

CPU offloading allows you to run larger models like CogVLM2 or Reka Flash 3, but it comes with a performance cost. Transferring data between the graphics card and system RAM is much slower than keeping everything in GDDR5 memory. This transfer bottleneck significantly reduces the generation speed of your local assistant.

The memory calculations for these models assume a standard 4k context window. As your conversation history grows, the model requires more memory to track the context. If you increase the context window beyond 4k tokens, the memory usage will rise. This extra memory demand can force a model that normally fits on your card to spill over into system RAM.